NumPy - Faster way to implement threshold value ceiling

Question

I'm writing a script to modify the luminance of a RGB image using NumPy and CV2 via converting from RGB to YCrCb and back again. However, the loop I'm using takes a while to execute, and am wondering if there is a faster way.

import cv2 as cv, numpy as np

threshold = 64
image = cv.imread("motorist.jpg", -1)
image.shape # Evaluates to (1000, 1500, 3)

im = cv.cvtColor(image, cv.COLOR_RGB2YCR_CB)

for row in image:
    for col in row:
        if col[0] > threshold:
            col[0] = threshold

image = cv.cvtColor(im, cv.COLOR_YCR_CB2RGB)
cv.imwrite("motorist_filtered.jpg", image)

That nested loop implementing the threshold comparison takes at least 5-7 seconds to execute. Is there a faster method to implement this functionality?

Answer 1

The idea is to create a mask that lets you use the numpy's vectorization. Since the shape is (n,m,3), loop over the first two dimensions and grab the first index of the last dimension with [:,:,0]

idx = image[:,:,0] > threshold
image[idx,0] = threshold

Answer 2

You can use clip:

• http://docs.scipy.org/doc/numpy/reference/generated/numpy.clip.html

Usage:

result = im.copy()
result[..., 0] = np.clip(im[..., 0], 0, threshold)

Or to modify in-place:

np.clip(im[..., 0], 0, threshold, out=im[..., 0])